Search

Industrial companies operate in increasingly competitive international environments; thus, they need to continuously innovate to improve their competitiveness, productivity, and quality. Digital transformation, which is one of the foundations of Industry 4.0, is essential to addressing these innovation challenges. The objective of this study is to present the methodology, development, and implementation of a new cloud computing platform that collects, stores, and processes data from shop floors. Manufacturing shop floors use connected, intelligent devices that produce thousands of data points that, once computed, provide a high added value. This study presents the architecture to collect this data, store it in a big data cloud computing solution, and then process it using advanced artificial intelligence algorithms and/or optimization techniques. The proposed platform has been developed to minimize the complexity and costs required to facilitate its adoption. The platform's implementation and evaluation were conducted by two companies from two different sectors of the Brazilian industry. The objective of the first company was to diagnose production losses in a compressor production line. Using the developed solution, the company identified prospective changes in the layout and automation that could increase productivity by approximately 5%. The objective of the second company was to implement dynamic and optimized process planning in clothing manufacturing. The first assessment of the gains of the proposed solution exhibited an average productivity increase of 10.69% ± 1.82% (confidence interval). [ABSTRACT FROM AUTHOR]